A miRNA-disease association prediction model based on tree-path global feature extraction and fully connected artificial neural network with multi-head self-attention mechanism.

BACKGROUND: MicroRNAs (miRNAs) emerge in various organisms, ranging from viruses to humans, and play crucial regulatory roles within cells, participating in a variety of biological processes. In numerous prediction methods for miRNA-disease associations, the issue of over-dependence on both similarity measurement data and the association matrix still hasn't been improved. In this paper, a miRNA-Disease association prediction model (called TP-MDA) based on tree path global feature extraction and fully connected artificial neural network (FANN) with multi-head self-attention mechanism is proposed. The TP-MDA model utilizes an association tree structure to represent the data relationships, multi-head self-attention mechanism for extracting feature vectors, and fully connected artificial neural network with 5-fold cross-validation for model training. RESULTS: The experimental results indicate that the TP-MDA model outperforms the other comparative models, AUC is 0.9714. In the case studies of miRNAs associated with colorectal cancer and lung cancer, among the top 15 miRNAs predicted by the model, 12 in colorectal cancer and 15 in lung cancer were validated respectively, the accuracy is as high as 0.9227. CONCLUSIONS: The model proposed in this paper can accurately predict the miRNA-disease association, and can serve as a valuable reference for data mining and association prediction in the fields of life sciences, biology, and disease genetics, among others.

Cluster energy prediction based on multiple strategy fusion whale optimization algorithm and light gradient boosting machine.

BACKGROUND: Clusters, a novel hierarchical material structure that emerges from atoms or molecules, possess unique reactivity and catalytic properties, crucial in catalysis, biomedicine, and optoelectronics. Predicting cluster energy provides insights into electronic structure, magnetism, and stability. However, the structure of clusters and their potential energy surface is exceptionally intricate. Searching for the global optimal structure (the lowest energy) among these isomers poses a significant challenge. Currently, modelling cluster energy predictions with traditional machine learning methods has several issues, including reliance on manual expertise, slow computation, heavy computational resource demands, and less efficient parameter tuning. RESULTS: This paper introduces a predictive model for the energy of a gold cluster comprising twenty atoms (referred to as Au20 cluster). The model integrates the Multiple Strategy Fusion Whale Optimization Algorithm (MSFWOA) with the Light Gradient Boosting Machine (LightGBM), resulting in the MSFWOA-LightGBM model. This model employs the Coulomb matrix representation and eigenvalue solution methods for feature extraction. Additionally, it incorporates the Tent chaotic mapping, cosine convergence factor, and inertia weight updating strategy to optimize the Whale Optimization Algorithm (WOA), leading to the development of MSFWOA. Subsequently, MSFWOA is employed to optimize the parameters of LightGBM for supporting the energy prediction of Au20 cluster. CONCLUSIONS: The experimental results show that the most stable Au20 cluster structure is a regular tetrahedron with the lowest energy, displaying tight and uniform atom distribution, high geometric symmetry. Compared to other models, the MSFWOA-LightGBM model excels in accuracy and correlation, with MSE, RMSE, and R2 values of 0.897, 0.947, and 0.879, respectively. Additionally, the MSFWOA-LightGBM model possesses outstanding scalability, offering valuable insights for material design, energy storage, sensing technology, and biomedical imaging, with the potential to drive research and development in these areas.

A lncRNA-disease association prediction model based on the two-step PU learning and fully connected neural networks.

Long non-coding RNAs (lncRNAs) have been shown to play a regulatory role in various processes of human diseases. However, lncRNA experiments are inefficient, time-consuming and highly subjective, so that the number of experimentally verified associations between lncRNA and diseases is limited. In the era of big data, numerous machine learning methods have been proposed to predict the potential association between lncRNA and diseases, but the characteristics of the associated data were seldom explored. In these methods, negative samples are randomly selected for model training and the model is prone to learn the potential positive association error, thus affecting the prediction accuracy. In this paper, we proposed a cyclic optimization model of predicting lncRNA-disease associations (COPTLDA in short). In COPTLDA, the two-step training strategy is adopted to search for the samples with the greater probability of being negative examples from unlabeled samples and the determined samples are treated as negative samples, which are combined together with known positive samples to train the model. The searching and training steps are repeated until the best model is obtained as the final prediction model. In order to evaluate the performance of the model, 30% of the known positive samples are used to calculate the model accuracy and 10% of positive samples are used to calculate the recall rate of the model. The sampling strategy used in this paper can improve the accuracy and the AUC value reaches 0.9348. The results of case studies showed that the model could predict the potential associations between lncRNA and malignant tumors such as colorectal cancer, gastric cancer, and breast cancer. The predicted top 20 associated lncRNAs included 10 colorectal cancer lncRNAs, 2 gastric cancer lncRNAs, and 8 breast cancer lncRNAs.

Effect of Trifolium pratense L. extracts on the bone resorption and osteogenic differentiation of osteoclasts and the underlying mechanisms / 中国组织工程研究

BACKGROUND: Isoflavone isolated from Trifolium pratense L. has been found to be able to effectively inhibit bone resorption, reduce bone turnover rate, improve osteocyte activity and bone mineral density by enhancing the effect of estrogen, which is helpful for the prevention and treatment of osteoporosis. OBJECTIVE: To investigate the effect of Trifolium pratense L. extracts on the bone resorption and differentiation of osteoclasts.METHODS: Rat bone marrow cells were extracted, isolated by lymphocyte separation and cultured for 5 hours; then, the non-adherent cells were selected followed by induced by 30 μg/L macrophage colony stimulating factor and 75 μg/L RANKL (control groups), or different concentrations of Trifolium pratense L. extracts (0.3, 0.6 and 1.2 g/L) to observe their effect on the osteoclast differentiation and bone resorption. The levels of osteoclast differentiation-associated proteins c-fos and NFATcl were determined by western blot assay. RESULTS AND CONCLUSION: Compared with the control group, different concentrations of Trifolium pratense L. extracts could suppress osteoclast differentiation and bone resorption to different degrees. Tartrate-resistant acid phosphatase staining showed that Trifolium pratense L. extracts could significantly reduce the number of osteoclasts. Western blot assay results suggest that Trifolium pratense L. extracts significantly inhibited the expression levels of c-fos and NFATcl. These results reveal that Trifolium pratense L. extracts can inhibit osteoclast differentiation and bone resorption.

Effects of Gross Saponins of Tribulus terrestris L. on Inflammatory Reaction and Permeability of Blood-brain Barrier in Rats Following Cerebral Ischemic Injury / 医药导报

Objective To explore the effects of gross saponins of Tribulus terrestris L.on inflammatory reaction and permeability of blood-brain barrier in rats following cerebral ischemia-reperfusion injury and their potential mechanisms. Methods Sixty SD rats were divided into sham operation group,model control group,gross saponins of Tribulus terrestris L.at low-dose (10 mg?kg-1 )and high-dose groups(30 mg?kg-1 ).Cerebral ischemia -reperfusion model was established with suture emboli method in middle cerebral artery of rats.Neural injury scores,the contents of Evans blue ( EB) and myeloperoxidase( MPO) activities in rat brain were measured 24 hours after the cerebral reperfusion post 2 h ischemia.Content of tumor necrosis factor-α (TNF-α) in rat brain was detected by ELISA; expression levels of matrix metalloproteinase-9(MMP-9) in rat brain was determined by Western blot. Results Compared to the model control group,the neurological deficit scores were significantly decreased(P<0.05),MPO activities and EB contents decreased(P< 0.05 or P< 0.01) in the treatment groups.The expression levels of TNF-α were significantly lower in the treatment groups(0.760±0.110) mg?g-1 and (0.670±0.073) mg?g-1 compared to (0.920±0.128) mg?g-1 in the model control group ( P< 0.05 or P< 0.01). The MMP-9 expression levels were (1.770± 0.181)% and(1.480±0.146)%,significantly lower than(2.200±0.186)% in the model control group(P<0.01). Conclusion Gross saponins of Tribulus terrestris L. exert neuroprotective effects on cerebral ischemia-reperfusion injury in rats through inhibiting the inflammatory reaction and decreasing the permeability of blood-brain barrier,which may be associated with the decrease of the TNF-α content and downregulation of the MMP-9 expression.

Effect of Panax quinquefolium saponins from steams and leaves on acidosis and the metabolism of free radical in focal cerebral ischemia injury rats / 中国基层医药

Objective To investigate the protective effct of Panax quinquefolium saponins from steams and leaves(PQS)on focal cerebral ischemia injury in rats and its mechanisms. Methods Wistar rats were randomly divided into sham operation group,model control group,nimodipine group and two PQS groups,in which PQS of 100 and 50 mg/kg was intragastrically administered. Focal cerebral ischemia model was established by middle cerebral artery occlusion (MCAO)in rats, via string ligation of artetia carotis interna. The content of malondicldehy de(MDA) was determined by thibabituric acid ( TBA ) test, the activity of lactate dehydrogenase ( LDH ), superoxide dismutase (SOD)and the content of lactic acid(LA) were detected by chemical colorimetry test in cerebral tissues. Results PQS( 100,50mg/kg)could significantly decrease the content of LA、MDA and increase the activity of LDH、SOD. Conclusion The protective mechanism of PQS on focal cerebral ischemia injury may be related to reduce acidosis, anti-free radical and resist oxidative damage.